[Experimental Feature] FP8 weight dtype for base model when running train_network (or sdxl_train_network)

library/train_util.py

@@ -2904,6 +2904,9 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
     parser.add_argument(
         "--full_bf16", action="store_true", help="bf16 training including gradients / 勾配も含めてbf16で学習する"
     )  # TODO move to SDXL training, because it is not supported by SD1/2
+    parser.add_argument(
+        "--fp8_base", action="store_true", help="use fp8 for base model / base modelにfp8を使う"
+    )
     parser.add_argument(
         "--ddp_timeout",
         type=int,

train_network.py

@@ -390,16 +390,36 @@ def train(self, args):
             accelerator.print("enable full bf16 training.")
             network.to(weight_dtype)
 
+        unet_weight_dtype = te_weight_dtype = weight_dtype
+        # Experimental Feature: Put base model into fp8 to save vram
+        if args.fp8_base:
+            assert (
+                torch.__version__ >= '2.1.0'
+            ), "fp8_base requires torch>=2.1.0 / fp8を使う場合はtorch>=2.1.0が必要です。"
+            assert (
+                args.mixed_precision != 'no'
+            ), "fp8_base requires mixed precision='fp16' or 'bf16' / fp8を使う場合はmixed_precision='fp16'または'bf16'が必要です。"
+            accelerator.print("enable fp8 training.")
+            unet_weight_dtype = torch.float8_e4m3fn
+            te_weight_dtype = torch.float8_e4m3fn
+
         unet.requires_grad_(False)
-        unet.to(dtype=weight_dtype)
+        unet.to(dtype=unet_weight_dtype)
         for t_enc in text_encoders:
             t_enc.requires_grad_(False)
+            t_enc.to(dtype=te_weight_dtype)
+            # nn.Embedding not support FP8
+            t_enc.text_model.embeddings.to(dtype=(
+                weight_dtype 
+                if te_weight_dtype == torch.float8_e4m3fn 
+                else te_weight_dtype
+            ))
 
         # acceleratorがなんかよろしくやってくれるらしい / accelerator will do something good
         if train_unet:
             unet = accelerator.prepare(unet)
         else:
-            unet.to(accelerator.device, dtype=weight_dtype)  # move to device because unet is not prepared by accelerator
+            unet.to(accelerator.device, dtype=unet_weight_dtype)  # move to device because unet is not prepared by accelerator
         if train_text_encoder:
             if len(text_encoders) > 1:
                 text_encoder = text_encoders = [accelerator.prepare(t_enc) for t_enc in text_encoders]
@@ -421,9 +441,6 @@ def train(self, args):
                 if train_text_encoder:
                     t_enc.text_model.embeddings.requires_grad_(True)
 
-            # set top parameter requires_grad = True for gradient checkpointing works
-            if not train_text_encoder:  # train U-Net only
-                unet.parameters().__next__().requires_grad_(True)
         else:
             unet.eval()
             for t_enc in text_encoders:
@@ -778,10 +795,17 @@ def remove_model(old_ckpt_name):
                         args, noise_scheduler, latents
                     )
 
+                    # ensure the hidden state will require grad
+                    if args.gradient_checkpointing:
+                        for x in noisy_latents:
+                            x.requires_grad_(True)
+                        for t in text_encoder_conds:
+                            t.requires_grad_(True)
+
                     # Predict the noise residual
                     with accelerator.autocast():
                         noise_pred = self.call_unet(
-                            args, accelerator, unet, noisy_latents, timesteps, text_encoder_conds, batch, weight_dtype
+                            args, accelerator, unet, noisy_latents.requires_grad_(train_unet), timesteps, text_encoder_conds, batch, weight_dtype
                         )
 
                     if args.v_parameterization: